Two-stage robust economic dispatch of distribution networks considering electric vehicles charging demand-price uncertainty correlations

With a large number of electric vehicles (EV) connected to the distribution networks (DN), the uncertainty of the charging load of EV brings challenges to the economic operation of DN. In this paper, the economic dispatch strategy of planning and operation is proposed to optimize the economy of DN. To better describe the correlation between EV charging demand and price uncertainty, a data-driven uncertainty set based on adjacent days is incorporated into two-stage robust optimization (TSRO) model. The proposed uncertainty set abandons the extreme scenario and is closer to the real-world scenarios. In the planning stage, charging piles are upgraded to devices with vehicle-to-grid (V2G) service capability, minimizing the planning cost. In the operational stage, the recourse method is adopted to quantify the worst output scenarios for wind, photovoltaic, and EV charging demands, which are reflected as the operating costs of the DN. The numerical simulations are carried out under the modified IEEE-33 bus system, and the nested column and constraint generation (N-C&CG) algorithm is adopted to solve the TSRO model under data-driven uncertainty sets. The results indicate that our proposed model decreases the total cost by 14% to 32% compared to other uncertainty models, which guarantees the economy of the decisions made by the model. The analysis demonstrates that the model possesses low conservatism while ensuring robustness and economy, and is suitable for long-term planning.